Outlier detection by robust principal components analysis

Caroni, C

dc.contributor.author	Caroni, C	en
dc.date.accessioned	2014-03-01T01:15:46Z
dc.date.available	2014-03-01T01:15:46Z
dc.date.issued	2000	en
dc.identifier.issn	0361-0918	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/13726
dc.subject	outlier tests	en
dc.subject	multivariate outliers	en
dc.subject	robust estimation	en
dc.subject	principal components analysis	en
dc.subject.classification	Statistics & Probability	en
dc.subject.other	MULTIPLE OUTLIERS	en
dc.subject.other	MULTIVARIATE DATA	en
dc.title	Outlier detection by robust principal components analysis	en
heal.type	journalArticle	en
heal.identifier.primary	10.1080/03610910008813606	en
heal.identifier.secondary	http://dx.doi.org/10.1080/03610910008813606	en
heal.language	English	en
heal.publicationDate	2000	en
heal.abstract	The robust principal components analysis (RPCA) introduced by Campbell (Applied Statistics 1980, 29, 231-237) provides in addition to robust versions of the usual output of a principal components analysis, weights for the contribution of each point to the robust estimation of each component. Low weights may thus be used to indicate outliers. The present simulation study provides critical values for testing the kth smallest weight in the RPCA of a sample of n p-dimensional vectors, under the null hypothesis of a multivariate normal distribution. The cases p=2(2)10, 15, 20 for n=20, 30, 40, 50, 75, 100 subject to n greater than or equal to p/2, are examined, with k less than or equal to root n.	en
heal.publisher	MARCEL DEKKER INC	en
heal.journalName	COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION	en
dc.identifier.doi	10.1080/03610910008813606	en
dc.identifier.isi	ISI:000085778500009	en
dc.identifier.volume	29	en
dc.identifier.issue	1	en
dc.identifier.spage	139	en
dc.identifier.epage	151	en